智能排课系统

# 模拟退火算法实现简单排课

import random

import math

# 定义课程信息

courses = [

{'name': '数学', 'teacher': '张老师', 'time': '08:00-10:00', 'room': 'A101'},

{'name': '英语', 'teacher': '李老师', 'time': '10:30-12:30', 'room': 'B202'},

{'name': '物理', 'teacher': '王老师', 'time': '14:00-16:00', 'room': 'C303'}

]

# 初始解（随机排列）

def initial_solution():

return random.sample(courses, len(courses))

# 计算适应度（评价排课质量）

def fitness(solution):

conflicts = 0

for i in range(len(solution)):

for j in range(i + 1, len(solution)):

if solution[i]['teacher'] == solution[j]['teacher'] and solution[i]['time'] == solution[j]['time']:

conflicts += 1

if solution[i]['room'] == solution[j]['room'] and solution[i]['time'] == solution[j]['time']:

conflicts += 1

return -conflicts # 最小化冲突

# 生成邻域解

def neighbor(solution):

idx1, idx2 = random.sample(range(len(solution)), 2)

solution[idx1], solution[idx2] = solution[idx2], solution[idx1]

return solution

# 模拟退火算法

def simulated_annealing():

current = initial_solution()

best = current

temperature = 1000

cooling_rate = 0.995

iterations = 1000

for _ in range(iterations):

next_solution = neighbor(current)

delta = fitness(next_solution) - fitness(current)

if delta > 0 or random.random() < math.exp(delta / temperature):

current = next_solution

if fitness(current) > fitness(best):

best = current

temperature *= cooling_rate

return best

# 执行算法并输出结果

result = simulated_annealing()

print("最佳排课方案：")

for course in result:

print(f"{course['name']} - {course['teacher']} - {course['time']} - {course['room']}")